1. Field of the Invention
This invention relates to independent component analysis processors, and particularly, to an independent component analysis processor that separates independent components from measured or monitored signals in real-time.
2. Description of Related Art
An Electroencephalography (EEG) is obtained by recording and amplifying weak biological electric signals generated by brain neural with medical instrument. A principle component analysis (PCA) and an independent component analysis (ICA) are two of the most popular in the art to process the brain wave signals. The ICA may separate noises from the brain wave signals.
U.S. Pat. No. 6,799,170 provides a system and method of separating signals. The system comprises a plurality of sensors that receive a mixture of source signals, a processor and an independent component analysis module. The processor takes samples of the mixture of source signals and stores each sample as a data vector to create a data set. The independent component analysis module performs an independent component analysis of the data vectors to separate an independent source signal from other signals in the mixture of source signals. The system, however, is bulky. U.S. Pat. No. 7,519,512 provides a dynamic blind signal separation method, which uses a Jacobi technique to decorrelate with small update angles to generate decorrelated orthonormal signals. The orthonormal signals are initialized and undergo an independent component analysis with small angle updates using statistics higher than second order to produce separated signals. The method, when embodied in a computer, is also bulky.
A document entitled “FPGA Implementation of FastICA based on Floating-Point Arithmetic Design for Real-Time Blind Source Separation” employs a field programmable gate array (FPGA) to realize a 2-channel FastICA in a floating-point design, to separate voice signals. A ping-pong memory hierarchy architecture is employed. A method thus provided for decomposing an eigenvector is not easy to be applied to three or more channels. Another document entitled “FPGA implementation of 4-channel ICA for on-line EEG signal separation”, though applicable to 4-channel ICA channels, does not have a pre-processing architecture, and thus convergence time is longer when the independent component analysis is performed. Therefore, how to realize a real-time operation of an independent component analysis with hardware, increase operation efficiency, and reduce the hardware cost is becoming one of the popular challenges in the signal processing field.